AI Bibliography
AI Bibliography |
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Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I., & Tygar, J. D. 2011, Adversarial machine learning. Paper presented at Proceedings of the 4th ACM workshop on Security and artificial intelligence. |
| Resource type: Proceedings Article BibTeX citation key: Huang2011 View all bibliographic details  |
Categories: Artificial Intelligence, Computer Science, Data Sciences, Decision Theory, General, Military Science Subcategories: Big data, Cyber, Deep learning, Game theory, Machine learning, Military research Creators: Huang, Joseph, Nelson, Rubinstein, Tygar Publisher: Collection: Proceedings of the 4th ACM workshop on Security and artificial intelligence |
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| Abstract |
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In this paper (expanded from an invited talk at AISEC 2010), we discuss an emerging field of study: adversarial machine learning---the study of effective machine learning techniques against an adversarial opponent. In this paper, we: give a taxonomy for classifying attacks against online machine learning algorithms; discuss application-specific factors that limit an adversary's capabilities; introduce two models for modeling an adversary's capabilities; explore the limits of an adversary's knowledge about the algorithm, feature space, training, and input data; explore vulnerabilities in machine learning algorithms; discuss countermeasures against attacks; introduce the evasion challenge; and discuss privacy-preserving learning techniques.
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